CREWS: Cortical Region Alignment for Enrollment Without Patient-Specific Parameters in Speech BCIs

Intracortical speech brain-computer interfaces (BCIs) offer a promising pathway to restore communication for individuals with severe speech disorders. However, conventional systems are predominantly patient-dependent and suffer from the scarcity of large-scale paired training data for each individual. Although transferring a decoder trained on previous patients could reduce this burden, cross-patient generalization remains challenging because electrode array placements cover distinct electrode indices and lack spatial alignment across subjects. Existing approaches typically mitigate this spatial mismatch by learning target-patient-specific input mappings, which fundamentally precludes zero-shot decoding. To overcome this limitation, we propose CREWS, a speech BCI framework that aligns neural recordings by common cortical regions rather than discrete electrode indices. CREWS reallocates each array index to its corresponding anatomical region slot and processes these inputs using shared, region-specific encoders. This design enables neural recordings from a novel patient to be decoded by parameters pre-trained on identical cortical regions, eliminating the need for patient-specific calibration parameters. In a transfer learning setup where a target patient shares only a single cortical region with the training cohort, CREWS pioneers viable zero-shot decoding, successfully demonstrating that cross-patient alignment without adaptation is attainable. Furthermore, with as few as 5 labeled adaptation sentences, CREWS achieves a phoneme error rate (PER) of 68.9%, outperforming baseline cross-patient methods that rely on patient-specific input layers. These results demonstrate that anatomical cortical-region alignment enables cross-patient transfer without patient-specific parameters and can substantially reduce the calibration data required to deploy intracortical speech BCIs.